hardware implementation of data compression ... - INFN Bologna

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52 Haar haar Wavelet function psi 1 0.5 0 −0.5 Data compression techniques 0 0.2 0.4 0.6 0.8 1 −1 Scaling function phi 1 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 Decomposition low−pass filter Decomposition high−pass filter 0 1 −0.5 0 0.5 0 1 −0.5 0 0.5 Reconstruction high−pass filter Reconstruction low−pass filter 0 1 −0.5 0 0.5 0 1 −0.5 0 0.5 Daubachies db1 db2 db3 db10 Wavelet function psi Scaling function phi Wavelet function psi Scaling function phi Wavelet function psi Scaling function phi Wavelet function psi 0 1 2 3 4 5 −1 −0.5 0 0.5 1 1.5 1.5 1 0.5 0 −0.5 −1 0 0.5 1 1.5 2 2.5 3 1 0.5 0 5 10 15 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 1 0.5 0 0.5 0.5 0 −0.5 0 0 −0.5 0 5 10 15 −1 0 1 2 3 4 5 0 0.5 1 1.5 2 2.5 3 0 0.2 0.4 0.6 0.8 1 −1 Scaling function phi 1 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 Decomposition low−pass filter Decomposition high−pass filter Decomposition low−pass filter Decomposition high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Decomposition low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Decomposition high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Decomposition low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Decomposition high−pass filter 0.5 0.5 0 0 0 2 4 6 8 10 12 14 16 18 −0.5 0 2 4 6 8 10 12 14 16 18 −0.5 0 1 −0.5 0 0.5 0 1 −0.5 0 0.5 Reconstruction high−pass filter Reconstruction low−pass filter Reconstruction high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Reconstruction low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Reconstruction high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Reconstruction low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Reconstruction high−pass filter Reconstruction low−pass filter 0.5 0.5 0 0 0 2 4 6 8 10 12 14 16 18 −0.5 0 2 4 6 8 10 12 14 16 18 −0.5 0 1 −0.5 0 0.5 0 1 −0.5 0 0.5 Figure 2.10: Some functions belonging to different wavelet families: note that db1 is equivalent to the Haar
Symlets sym2 sym3 sym4 sym8 Wavelet function psi Scaling function phi Wavelet function psi Scaling function phi Wavelet function psi Scaling function phi 2.6 — Implementation of compression algorithms 1 0 1 2 3 4 5 6 7 −1 −0.5 0 0.5 1 1.5 Wavelet function psi 0 1 2 3 4 5 6 7 −0.2 0 0.2 0.4 0.6 0.8 1 1.2 Scaling function phi 0 1 2 3 4 5 −1.5 −1 −0.5 0 0.5 1 1 0 0.5 1 1.5 2 2.5 3 −1.5 −1 −0.5 0 0.5 1 1 0.5 0.5 0.5 0 0 −0.5 0 0 5 10 15 0 5 10 15 −0.2 0 0.2 0.4 0.6 0.8 1 0 1 2 3 4 5 0 0.5 1 1.5 2 2.5 3 Decomposition high−pass filter Decomposition low−pass filter Decomposition high−pass filter Decomposition low−pass filter 0 2 4 6 8 10 12 14 −0.5 0 0.5 0 2 4 6 8 10 12 14 −0.5 0 0.5 0.5 0 −0.5 0 1 2 3 4 5 6 7 0.5 0 −0.5 0 1 2 3 4 5 6 7 Decomposition high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Decomposition low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Decomposition high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Decomposition low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Reconstruction high−pass filter Reconstruction low−pass filter Reconstruction high−pass filter Reconstruction low−pass filter 0 2 4 6 8 10 12 14 −0.5 0 0.5 0 2 4 6 8 10 12 14 −0.5 0 0.5 0.5 0 −0.5 0 1 2 3 4 5 6 7 0.5 0 −0.5 0 1 2 3 4 5 6 7 Reconstruction high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Reconstruction low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 4 5 Reconstruction high−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Reconstruction low−pass filter 0.8 0.6 0.4 0.2 0 −0.2 −0.4 0 1 2 3 Wavelet function psi Scaling function phi Wavelet function psi Scaling function phi Wavelet function psi Wavelet function psi Scaling function phi 1.5 0 1 2 3 4 5 −1 −0.5 0 0.5 1 1.5 2 1.5 1 1 1 0.5 1 0.5 0.5 0 0 0.5 0 −0.5 −0.5 −0.5 0 2 4 6 8 10 −0.2 0 0.2 0.4 0.6 0.8 1 1.2 Scaling function phi 0 0 5 10 15 20 25 0 5 10 15 20 25 −0.2 0 0.2 0.4 0.6 0.8 1 0 5 10 15 0 5 10 15 −0.2 0 0.2 0.4 0.6 0.8 1 0 2 4 6 8 10 0 1 2 3 4 5 Decomposition high−pass filter Decomposition low−pass filter Decomposition high−pass filter Decomposition low−pass filter Decomposition high−pass filter Decomposition low−pass filter Decomposition high−pass filter Decomposition low−pass filter 0 4 8 12 16 20 24 28 −0.5 0 0.5 0 4 8 12 16 20 24 28 −0.5 0 0.5 0 2 4 6 8 10 12 14 16 −0.5 0 0.5 0 2 4 6 8 10 12 14 16 −0.5 0 0.5 0 2 4 6 8 10 −0.5 0 0.5 0 2 4 6 8 10 −0.5 0 0.5 0.5 0.5 0 −0.5 0 −0.5 0 1 2 3 4 5 0 1 2 3 4 5 Reconstruction low−pass filter Reconstruction high−pass filter Reconstruction low−pass filter Reconstruction high−pass filter Reconstruction low−pass filter Reconstruction high−pass filter Reconstruction low−pass filter 0 4 8 12 16 20 24 28 −0.5 0 0.5 0 4 8 12 16 20 24 28 −0.5 0 0.5 0 2 4 6 8 10 12 14 16 −0.5 0 0.5 0 2 4 6 8 10 12 14 16 −0.5 0 0.5 0 2 4 6 8 10 −0.5 0 0.5 0 2 4 6 8 10 −0.5 0 0.5 Figure 2.11: Some functions belonging to different wavelet families Coiflets coif1 coif2 coif3 coif5 Reconstruction high−pass filter 0.5 0.5 0 −0.5 0 −0.5 0 1 2 3 4 5 0 1 2 3 4 5 53
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UNIVERSITÀ DEGLI STUDI DI BOLOGNA
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Contents Introduction ix 1 The ALIC
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CONTENTS 4 2D compression algorithm
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Introduction This thesis work has b
Page 13 and 14: Chapter 1 The ALICE experiment ALIC
Page 15 and 16: 1.1 — The Inner Tracking System 1
Page 17 and 18: 1.1 — The Inner Tracking System t
Page 19 and 20: 1.1 — The Inner Tracking System P
Page 21 and 22: 1.2 — Design of the drift layers
Page 23 and 24: 1.3 — The SDDs (Silicon Drift Det
Page 25 and 26: PASCAL AMBRA 1.4 — SDD readout sy
Page 27 and 28: data_in[0] data_in[1] data_in[2] da
Page 29 and 30: 1.4 — SDD readout system 1.4.2 Ev
Page 31 and 32: 1.4 — SDD readout system with a c
Page 33 and 34: Chapter 2 Data compression techniqu
Page 35 and 36: 2.2 — Remarks on information theo
Page 37 and 38: 2.3 — Compression techniques 2.3.
Page 39 and 40: 2.4 — Lossless compression techni
Page 45 and 46: Original sequence Sequence after di
Page 47 and 48: 2.5 — Lossy compression technique
Page 63: 2.6 — Implementation of compressi
Page 67 and 68: Chapter 3 1D compression algorithm
Page 69 and 70: 3.2 — 1D compression algorithm Fi
Page 71 and 72: 3.3 — 1D algorithm performances s
Page 73 and 74: 3.3 — 1D algorithm performances F
Page 75 and 76: 3.4 — CARLOS v1 Figure 3.6: CARLO
Page 77 and 78: 3.4 — CARLOS v1 Figure 3.7: Pictu
Page 79 and 80: 3.4 — CARLOS v1 3.4.3 Functions p
Page 81 and 82: 3.5 — CARLOS v2 have a 100% compa
Page 83 and 84: 3.5 — CARLOS v2 3.5.1 The firstch
Page 85 and 86: 3.5 — CARLOS v2 data and when loa
Page 87 and 88: 3.5 — CARLOS v2 which of the 8 qu
Page 89 and 90: - indat6 : input 32-bit bus; - inda
Page 91 and 92: 3.5 — CARLOS v2 - femux is a mult
Page 93 and 94: 3.5 — CARLOS v2 total optical fib
Page 95 and 96: 3.5 — CARLOS v2 Figure 3.11: Desi
Page 97 and 98: 3.5 — CARLOS v2 that CARLOS will
Page 99 and 100: 3.6 — CARLOS v2 design flow Figur
Page 101 and 102: 3.7 — Tests performed on CARLOS v
Page 103 and 104: Chapter 4 2D compression algorithm
Page 111 and 112: 4.2 — CARLOS v3 vs. the previous
Page 113 and 114: 4.3 — The final readout architect
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4.5 — CARLOS v3 building blocks -
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4.5 — CARLOS v3 building blocks T
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4.5 — CARLOS v3 building blocks F
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4.5 — CARLOS v3 building blocks c
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4.5 — CARLOS v3 building blocks o
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4.5 — CARLOS v3 building blocks u
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4.5 — CARLOS v3 building blocks a
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4.5 — CARLOS v3 building blocks p
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4.7 — CARLOS layout features post
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Chapter 5 Wavelet based compression
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levels used: 5.1 — Wavelet based
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5.2 — Multiresolution algorithm o
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5.3 — Choice of the architecture
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5.3 — Choice of the architecture
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Dettaglio 1 1 D1 2 Downsample Hi_De
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2 Upsample Dettaglio 1 1 D1 Hi_Rec
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5.4 — Multiresolution algorithm p
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5.5 — Hardware implementation ter
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5.5 — Hardware implementation Fig
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5.5 — Hardware implementation com
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5.5 — Hardware implementation Haa
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Conclusions The main goal of this t
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Bibliography [1] ALICE Collaboratio
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BIBLIOGRAPHY [19] D. Cavagnino, P.
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